Diffusion Spectrum Imaging (“DSI”) (see, e.g., Reference 1) provides a robust estimation of intra-voxel fiber tract crossings (see, e.g., Reference 2), facilitating accurate modeling of the white-matter wiring of the human brain. Higher order diffusion acquisitions, such as DSI (see, e.g., Reference 9), are invaluable tools for the non-invasive study of white matter connectivity. Indeed, complex intra-voxel fiber crossings are captured in an Orientation Distribution Function (“ODF”). (See, e.g., References 9 and 10). Additionally, recent sequence improvements have reduced acquisition times, making DSI a practical tool for neuroscience research (see, e.g., References 11 and 12). This evolution has highlighted the need for a robust methodology for statistical analysis of group ODF datasets.
One previous method contrasts subject ODF values to those of a normal population (see, e.g., Reference 13); however this prior approach is limited to predetermined skeletons of fiber directions. Other methods focus on the connectome level, evaluating differences in structural connections on a local (see, e.g., Reference 14) or global (see, e.g., Reference 15) level, thus possibly missing more subtle differences in diffusion behavior captured in the ODF.
Diffusion weighted (“DW”) magnetic resonance imaging (“MRI”) samples the diffusive displacement of water, and its interactions with cellular structures, such as axon membranes in in vivo white matter. (See, e.g., References 22 and 23). By encoding the anisotropic tissue micro-structure, DW MRI provides insight in the complex white matter tract architecture. Highly detailed High Angular Resolution Diffusion Imaging (“HARDI”), (see, e.g., Reference 24)) methods such as DSI (see, e.g., References 22 and 25-27) and Q-ball imaging (see, e.g., Reference 28), facilitate the capture of the complex fiber crossings in each voxel (see, e.g., References 26 and 29) in ODF. (See, e.g., Reference 22). Combinations of these voxel-wise ODFs over the brain supplies tractography procedures with an estimate of the connection infrastructure of the brain. While tractography procedures continue to evolve, the connection information they provide has been successfully used for depicting changes in brain research (see, e.g., References 30 and 31) and pathologically relevant conditions. (See, e.g., Reference 29).
Widespread adoption of HARDI datasets in group studies has been hindered by the long acquisition times needed for the large number of q-space samples utilized for sufficient angular resolution. (See, e.g., References 32 and 33). Recent developments in simultaneous multi-slice or multiband procedures (see, e.g., References 34 and 35) and sequence design (see, e.g., References 36-38) have led to data acquisition times that, for the first time, make HARDI a viable and practical tool for clinical applications and neuroscience research. This evolution has highlighted the need for a robust methodology for statistical analysis of group ODF data sets.
A number of methods have previously been proposed to identify and study differences in the diffusion signals of groups of subjects. Diffusion-specific Voxel-Based analysis (“VBA”) methods register quantitative diffusion measures for the whole brain (see, e.g., Reference 39) or project them on a tract skeleton, for example, Tract-Based spatial Statistics (“TBSS”) (see, e.g., References 40 and 41) or surface. (See, e.g., Reference 42). Most of these approaches are based on information gained from Diffusion Tensor Imaging (“DTI”) (see, e.g., Reference 23), an incomplete representation of the complex intra-voxel crossings. (See, e.g., Reference 43). This is partially mitigated by an extension of the TBSS-method accommodating two crossing fibers. (See, e.g., Reference 41). Nevertheless, the focus of these methods on DTI makes them ill-suited to fully exploit the much higher dimensionality of the ODFs. In addition, the projection based methods suffer from inaccurate tract representations and projections. (See, e.g., References 44 and 45).
Other methods use tractography results to identify structurally connected fiber populations globally or locally. (See, e.g., References 45-49). The resulting connectivity matrices can then be used directly for statistical tests (see, e.g., References 46, 47 and 50) or the tractograms can inform tract-specific smoothing (see, e.g., References 45 and 48) and enhancement of statistical maps along the tracts (see, e.g., References 45, 48 and 49) using Threshold-Free Cluster Enhancement approaches (“TFCE”) (see, e.g., Reference 51). While these tractography based methods are powerful, they suffer from problems related to imperfections in the tractography (see, e.g., References 52-54); some limit the identified fiber directions to a predefined template (see, e.g., Reference 49) and they generally miss more subtle differences in diffusion behavior captured in the ODF.
Various methods for group difference identification in diffusion Mill studies capitalize on the information contained in the ODFs registered to a common atlas. Early work used voxel-wise whole brain multivariate statistics on the coefficients of the ODFs spherical harmonics representations. (See, e.g., Reference 55). The first work to mine the high dimensionality of the whole ODF rather than a representation, applied Principal Component Analysis (“PCA”) to identify the defining ODF features in each voxel in a whole brain group analysis. (See, e.g., Reference 47). In each voxel, the Principal Components (“PC”) represent an orthogonal basis of ODF features that are common within, common between or different between subject cohorts. Statistical analysis of the weights of the PCs, the PC-scores, then informs the significance of group differences. (See, e.g., Reference 47). However, PCA is sensitive to outliers and can be easily corrupted by the individual variability of subjects (see, e.g., References 56 and 57), reducing the power of the statistical test.
Thus, it may be beneficial to provide an exemplary system, method and computer-accessible medium for diffusion imaging acquisition and analysis which can overcome at least some of the deficiencies described herein above.